[ONNX] Update Reducesum operator for opset 13

test/onnx/test_pytorch_onnx_onnxruntime.py

@@ -2754,7 +2754,6 @@ def forward(self, input):
         x = torch.randn(4, 5, dtype=torch.float)
         self.run_test(ReducedOpModule(), x)
 
-    @skipIfUnsupportedOpsetVersion([13])
     def test_reduced_sum(self):
         return self._test_reduced_ops(op=torch.sum)
 
@@ -4314,7 +4313,6 @@ def forward(self, input):
 
     @disableScriptTest()  # error in propagate as assign input shape
     @skipIfUnsupportedMinOpsetVersion(10)
-    @skipIfUnsupportedOpsetVersion([12, 13])  # Due to ONNX Loop shape inference issue
     def test_embedding_bag(self):
         model = torch.nn.EmbeddingBag(10, 5, mode='sum', scale_grad_by_freq=True)
         input = torch.randint(10, (7,))
@@ -4331,7 +4329,6 @@ def test_embedding_bag(self):
         self.run_test(model, (input))
 
     @skipIfUnsupportedMinOpsetVersion(11)
-    @skipIfUnsupportedOpsetVersion([12, 13])  # Due to ONNX Loop shape inference issue
     def test_embedding_bag_1d_per_sample_weights(self):
         class EmbeddingModel(torch.nn.Module):
             def forward(self, embedding_matrix, input, offset, weights):
@@ -4346,7 +4343,6 @@ def forward(self, embedding_matrix, input, offset, weights):
         self.run_test(model, (embedding_matrix, x, offset, w))
 
     @skipIfUnsupportedMinOpsetVersion(11)
-    @skipIfUnsupportedOpsetVersion([12, 13])  # Due to ONNX Loop shape inference issue
     def test_embedding_bag_2d_per_sample_weights(self):
         class EmbeddingModel(torch.nn.Module):
             def forward(self, embedding_matrix, input, weights):

torch/onnx/symbolic_helper.py

@@ -335,6 +335,17 @@ def _squeeze_helper(g, input, axes_i):
     else:
         return g.op("Squeeze", input, axes_i=axes_i)
 
+def _reducesum_helper(g, input, axes_i=None, keepdims_i=1, noop_with_empty_axes_i=0):
+    keepdims_i = _maybe_get_const(keepdims_i, 'i')
+    if _export_onnx_opset_version >= 13:
+        if axes_i:
+            if not _is_value(axes_i):
+                axes_i = g.op("Constant", value_t=torch.tensor(axes_i, dtype=torch.long))
+            return g.op("ReduceSum", input, axes_i, keepdims_i=keepdims_i, noop_with_empty_axes_i=noop_with_empty_axes_i)
+        return g.op("ReduceSum", input, keepdims_i=keepdims_i, noop_with_empty_axes_i=noop_with_empty_axes_i)
+    else:
+        return g.op("ReduceSum", input, axes_i=axes_i, keepdims_i=keepdims_i)
+
 def _interpolate_size_to_scales(g, input, output_size, dim):
     output_size = _maybe_get_const(output_size, 'is')
     if _is_value(output_size):

torch/onnx/symbolic_opset10.py

@@ -231,7 +231,7 @@ def embedding_bag(g,
                 per_sample_weights_row = sym_help._unsqueeze_helper(g, per_sample_weights_row, [1])
                 embeddings = g.op("Mul", embeddings, per_sample_weights_row)
             if mode == 0:
-                embeddings = g.op("ReduceSum", embeddings, axes_i=[0], keepdims_i=0)
+                embeddings = sym_help._reducesum_helper(g, embeddings, axes_i=[0], keepdims_i=0)
             elif mode == 1:
                 embeddings = g.op("ReduceMean", embeddings, axes_i=[0], keepdims_i=0)
             else:

torch/onnx/symbolic_opset11.py

@@ -935,7 +935,7 @@ def embedding_bag(g,
         per_sample_weights_row = sym_help._unsqueeze_helper(loop_block, per_sample_weights_row, [1])
         embeddings = loop_block.op("Mul", embeddings, per_sample_weights_row)
     if mode == 0:
-        embeddings = loop_block.op("ReduceSum", embeddings, axes_i=[0], keepdims_i=0)
+        embeddings = sym_help._reducesum_helper(loop_block, embeddings, axes_i=[0], keepdims_i=0)
     elif mode == 1:
         embeddings = loop_block.op("ReduceMean", embeddings, axes_i=[0], keepdims_i=0)
     else:

torch/onnx/symbolic_opset13.py

@@ -2,15 +2,16 @@
 # see Note [Edit Symbolic Files] in symbolic_helper.py
 
 # This file exports ONNX ops for opset 13
-from torch.onnx.symbolic_helper import _block_list_in_opset
 import torch
 import torch.onnx.symbolic_helper as sym_help
-from torch.onnx.symbolic_helper import parse_args
+from torch.onnx.symbolic_helper import parse_args, _unimplemented
+from torch.onnx.symbolic_opset9 import overload_by_arg_count, _maybe_cast_reduce_op_input
 
-block_listed_operators = ['embedding_bag']
 
-for block_listed_op in block_listed_operators:
-    vars()[block_listed_op] = _block_list_in_opset(block_listed_op)
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in symbolic_helper.py
+
+# This file exports ONNX ops for opset 13
 
 
 @parse_args('v', 'i', 'none')
@@ -38,7 +39,7 @@ def frobenius_norm(g, self, dim=None, keepdim=False):
     if not sym_help._is_value(dim_val) and len(dim_val) == 0:
         return g.op("ReduceL2", self, keepdims_i=0)
     sqr = g.op('Mul', self, self)
-    sumsqr = g.op('ReduceSum', sqr, dim, keepdims_i=keepdim)
+    sumsqr = sym_help._reducesum_helper(g, sqr, dim, keepdims_i=keepdim)
     return g.op('Sqrt', sumsqr)
 
 
@@ -108,3 +109,36 @@ def unbind(g, self, dim=0, _outputs=None):
 def glu(g, input, dim):
     first, second = g.op('Split', input, dim, outputs=2)
     return g.op('Mul', first, g.op('Sigmoid', second))
+
+
+def _reduce_op_symbolic(onnx_op_name):
+    def symbolic(g, self, dim=None, keepdim=None):
+        self = _maybe_cast_reduce_op_input(g, self)
+        if dim is None:
+            # all-reduce path
+            return g.op(onnx_op_name, self, keepdims_i=0)
+        else:
+            keepdim = sym_help._get_const(keepdim, 'i', 'keepdim')
+            return g.op(onnx_op_name, self, dim, keepdims_i=keepdim)
+    return symbolic
+
+def _reduce_with_dtype(onnx_op, name):
+    symbolic = _reduce_op_symbolic(onnx_op)
+
+    @overload_by_arg_count
+    def reduce(g, *args, **kwargs):
+        @parse_args('v', 'none')
+        def reduce_nodim(g, self, dtype):
+            if dtype.node().kind() != 'prim::Constant':
+                return _unimplemented(name, "dtype")
+            return symbolic(g, self)
+
+        @parse_args('v', 'v', 'i', 'none')
+        def reduce_dim(g, self, dim, keepdim, dtype):
+            if dtype.node().kind() != 'prim::Constant':
+                return _unimplemented(name, "dtype")
+            return symbolic(g, self, dim, keepdim)
+        return reduce_nodim, reduce_dim
+    return reduce
+
+sum = _reduce_with_dtype('ReduceSum', 'sum')

torch/onnx/symbolic_opset9.py

@@ -753,7 +753,7 @@ def softmax(g, input, dim, dtype=None):
     input = g.op('Sub', input, g.op('ReduceMax', input, axes_i=[dim], keepdims_i=1))
 
     exp = g.op('Exp', input)
-    sum = g.op('ReduceSum', exp, axes_i=[dim])
+    sum = sym_help._reducesum_helper(g, exp, axes_i=[dim])
     softmax = g.op('Div', exp, sum)
     if dtype and dtype.node().kind() != 'prim::Constant':
         parsed_dtype = sym_help._get_const(dtype, 'i', 'dtype')
@@ -2383,7 +2383,7 @@ def gather(g, self, dim, index, sparse_grad=False):
     depth = size(g, self, g.op("Constant", value_t=torch.LongTensor([dim])))
     index = g.op("Cast", g.op("OneHot", index, depth, values, axis_i=dim), to_i=sym_help.cast_pytorch_to_onnx[dtype])
     mul = g.op("Mul", sym_help._unsqueeze_helper(g, self, [dim + 1]), index)
-    return g.op("ReduceSum", mul, axes_i=[dim], keepdims_i=0)
+    return sym_help._reducesum_helper(g, mul, axes_i=[dim], keepdims_i=0)
 
 
 @parse_args('v', 'is', 'b', 'i')
@@ -2639,7 +2639,7 @@ def try_mask_to_index(index):
 @parse_args('v', 'is', 'i')
 def frobenius_norm(g, self, dim=None, keepdim=False):
     sqr = g.op('Mul', self, self)
-    sumsqr = g.op('ReduceSum', sqr, axes_i=dim, keepdims_i=keepdim)
+    sumsqr = sym_help._reducesum_helper(g, sqr, axes_i=dim, keepdims_i=keepdim)
     return g.op('Sqrt', sumsqr)
 
 
@@ -2805,7 +2805,7 @@ def kl_div(g, input, target, reduction, log_target):
     elif reduction == 1:
         return g.op("ReduceMean", output, keepdims_i=0)
     elif reduction == 2:
-        return g.op("ReduceSum", output, keepdims_i=0)
+        return sym_help._reducesum_helper(g, output, keepdims_i=0)
     else:
         return sym_help._onnx_unsupported("kl_div with reduction other than none, mean, or sum. Please open a bug to "
                                           "request ONNX export support for the missing reduction type.")